Using Homomorphic Encryption For Privacy-Preserving Collaborative Decision Tree Classification

Author:

Zhan Justin

Publisher

IEEE

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PrivaTree: Collaborative Privacy-Preserving Training of Decision Trees on Biomedical Data;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2023

2. HERB+: Evolving an Industrial-Strength Privacy-Preserving Machine Learning Framework;2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing (PRDC);2022-11

3. Herb: Privacy-preserving Random Forest with Partially Homomorphic Encryption;2022 International Joint Conference on Neural Networks (IJCNN);2022-07-18

4. SecDT: Privacy-Preserving Outsourced Decision Tree Classification Without Polynomial Forms in Edge-Cloud Computing;IEEE Transactions on Signal and Information Processing over Networks;2022

5. Privacy-preserving Machine Learning Based on Homomorphic Conjugate Search Problem;2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS);2021-12-17

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